Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
The current study evaluates the financial innovation life cycle for renewable energy investments.A novel model is proposed that has two stages.First,the financial innovation life cycle is weighted by the two-generatio...The current study evaluates the financial innovation life cycle for renewable energy investments.A novel model is proposed that has two stages.First,the financial innovation life cycle is weighted by the two-generation technology S-curve(TTSC)for renewable energy investments.Second,the TTSC is ranked with integer patterns for renewable energy investments.For this purpose,the decision-making trial and evaluation laboratory(DEMATEL)is considered with q-rung orthopair fuzzy sets(q-ROFSs).A comparative examination is then performed using intuitionistic and Pythagorean fuzzy sets,and we find similar results for all fuzzy sets.Hence,the suggested model is reliable and coherent.Maturity phase 1 is the most significant phase of the financial innovation life cycle for these projects.Aging is the most important period for financial innovation in renewable energy investment projects—renewable energy companies should make strategic decisions after that point.In this situation,decisions should relate to either radical or incremental innovation.If companies do not make decisions during these phases,innovative financial products cannot be improved.As a result,renewable energy companies will not prefer financing products.展开更多
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金partially supported by State key R&D Program of China(#2020YFC0832702).
文摘The current study evaluates the financial innovation life cycle for renewable energy investments.A novel model is proposed that has two stages.First,the financial innovation life cycle is weighted by the two-generation technology S-curve(TTSC)for renewable energy investments.Second,the TTSC is ranked with integer patterns for renewable energy investments.For this purpose,the decision-making trial and evaluation laboratory(DEMATEL)is considered with q-rung orthopair fuzzy sets(q-ROFSs).A comparative examination is then performed using intuitionistic and Pythagorean fuzzy sets,and we find similar results for all fuzzy sets.Hence,the suggested model is reliable and coherent.Maturity phase 1 is the most significant phase of the financial innovation life cycle for these projects.Aging is the most important period for financial innovation in renewable energy investment projects—renewable energy companies should make strategic decisions after that point.In this situation,decisions should relate to either radical or incremental innovation.If companies do not make decisions during these phases,innovative financial products cannot be improved.As a result,renewable energy companies will not prefer financing products.
